Discriminative acoustic language recognition via channel-compensated GMM statistics
Niko Brümmer, Albert Strasheim, Valiantsina Hubeika, Pavel Matějka, Lukáš Burget, Ondřej Glembek · 2009
We propose a novel design for acoustic feature-based automatic spoken language recognizers. Our design is inspired by recent advances in text-independent speaker recognition, where intraclass variability is modeled by factor analysis in Gaussian mixture model (GMM) space. We use approximations to GMMlikelihoods which allow variable-length data sequences to be represented as statistics of fixed size. Our experiments on NIST LRE’07 show that variability-compensation of these statistics can reduce error-rates by a factor of three. Finally, we show that further improvements are possible with discriminative logistic regression training. Index Terms: acoustic language recognition, intersession variability compensation, discriminative training